Electronic computing devices are becoming increasingly ubiquitous in the modern world. Whether utilized for business, entertainment, communication, security or numerous other purposes, the capabilities of such devices continue to expand. Along with the improvements made in terms of processing power, rendering technology, memory, power consumption and other aspects, various applications have also been developed to utilize the expanded capabilities of computing devices. However, the expansion of capabilities with respect to such devices has also introduced new sets of challenges as further improvements are sought and new applications are developed.
One area in which the use of electronic computing devices has presented new challenges relates to computer vision. Computer vision utilizes machines to see. As such, for example, computer vision often employs cameras and other elements to build systems that can obtain information from image data such as a video sequence, views from multiple cameras or multidimensional data from scanning devices. Computer vision may be useful for many tasks such as: controlling processes or device movements; detecting and/or recognizing events, objects, patterns or people; organizing information; and/or the like. Accordingly, computer vision may be considered to be an artificial vision system, which may be implemented in combinations of various devices and applications.
One common situation encountered in computer vision relates to the fitting of noisy data with parametric models in the presence of high ratio noise. Existing technologies such as Hough transformations, RANSAC (random sample consensus), and improvements of these technologies are often utilized in such situations. However, Hough transformation and RANSAC based approaches are typically employed best in environments with known noise levels. In particular, both Hough transformation and RANSAC based approaches require the entry of a threshold or other user-specified control parameters, which may be difficult for users to estimate when noise levels are unknown. Accordingly, improvements in the area of pattern detection in environments with unknown noise levels may be desirable.